{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import decomposition, pipeline, metrics, grid_search\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rmsle(h, y): \n",
    "    \"\"\"\n",
    "    Compute the Root Mean Squared Log Error for hypthesis h and targets y\n",
    "\n",
    "    Args:\n",
    "        h - numpy array containing predictions with shape (n_samples, n_targets)\n",
    "        y - numpy array containing targets with shape (n_samples, n_targets)\n",
    "    \"\"\"\n",
    "    return np.sqrt(np.square(np.log(h + 1) - np.log(y + 1)).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weather = pd.read_csv('weather/weather_ready_to_use.csv')\n",
    "weather['visit_date'] = pd.to_datetime(weather['visit_date'],format= '%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "air_reserve = pd.read_csv('air_reserve.csv')\n",
    "hpg_reserve = pd.read_csv('hpg_reserve.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mean_day_wise = pd.read_csv('mean_day_wise.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hpg_store_info = pd.read_csv('hpg_store_info.csv')\n",
    "air_store_info = pd.read_csv('air_store_info.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'hpg_store_id', u'hpg_genre_name', u'hpg_area_name', u'latitude',\n",
       "       u'longitude'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hpg_store_info.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n1 = hpg_store_info.groupby('hpg_genre_name').agg('count')['hpg_store_id'].reset_index()\n",
    "n1.columns = ['hpg_genre_name','air_genre_name_c']\n",
    "\n",
    "n2 = hpg_store_info.groupby('hpg_area_name').agg('count')['hpg_store_id'].reset_index()\n",
    "n2.columns = ['hpg_area_name','air_area_name_c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TfidfVectorizer(analyzer='word', binary=False, decode_error=u'strict',\n",
       "        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',\n",
       "        lowercase=True, max_df=1.0, max_features=None, min_df=3,\n",
       "        ngram_range=(1, 5), norm=u'l2', preprocessor=None, smooth_idf=1,\n",
       "        stop_words='english', strip_accents='unicode', sublinear_tf=1,\n",
       "        token_pattern='\\\\w{1,}', tokenizer=None, use_idf=1,\n",
       "        vocabulary=None)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_tf = list(air_store_info.apply(lambda x:'%s %s' % (x['air_area_name'],x['air_genre_name']),axis=1))\n",
    "\n",
    "tfv = TfidfVectorizer(min_df=3,  max_features=None, \n",
    "            strip_accents='unicode', analyzer='word',token_pattern=r'\\w{1,}',\n",
    "            ngram_range=(1, 5), use_idf=1,smooth_idf=1,sublinear_tf=1,\n",
    "            stop_words = 'english')\n",
    "tfv.fit(air_tf)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "air_visit_data = pd.read_csv('air_visit_data.csv')\n",
    "sample_submission = pd.read_csv('sample_submission.csv')\n",
    "sample_submission['air_store_id'] = sample_submission['id'].apply(lambda x: x.split('_')[0]+str('_') +x.split('_')[1])\n",
    "sample_submission['visit_date'] = sample_submission['id'].apply(lambda x: x.split('_')[2])\n",
    "sample_submission['visit_date'] = pd.to_datetime(sample_submission['visit_date'],format= '%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "air_visit_data = air_visit_data.merge(air_store_info,how = 'left',on= 'air_store_id')\n",
    "sample_submission = sample_submission.merge(air_store_info,how = 'left',on= 'air_store_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2016-01-01 00:00:00')"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_visit_data['id'] = air_visit_data['air_store_id'] + str('_') + air_visit_data['visit_date']\n",
    "new_train = pd.read_csv('new_train.csv')\n",
    "air_visit_data = air_visit_data.merge(new_train,on='id',how= 'left')\n",
    "air_visit_data = air_visit_data.drop('id',axis=1)\n",
    "air_visit_data['visit_date'] = pd.to_datetime(air_visit_data['visit_date'],format= '%Y-%m-%d')\n",
    "air_visit_data.visit_date.min()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_test =  pd.read_csv('new_test.csv')\n",
    "sample_submission =  sample_submission.merge(new_test,how = 'left',on='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\ndef f(x):\\n    try:\\n        return x.split('-')[2]\\n    except:\\n        return -1\\n\\nair_store_info['lol3'] = air_store_info.air_area_name.apply(f)\\nair_store_info['lol1'] = air_store_info.air_area_name.apply(lambda x: x.split('-')[0])\\nair_store_info['lol2'] = air_store_info.air_area_name.apply(lambda x: x.split('-')[1])\\n\""
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "def f(x):\n",
    "    try:\n",
    "        return x.split('-')[2]\n",
    "    except:\n",
    "        return -1\n",
    "\n",
    "air_store_info['lol3'] = air_store_info.air_area_name.apply(f)\n",
    "air_store_info['lol1'] = air_store_info.air_area_name.apply(lambda x: x.split('-')[0])\n",
    "air_store_info['lol2'] = air_store_info.air_area_name.apply(lambda x: x.split('-')[1])\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'air_store_id', u'air_genre_name', u'air_area_name', u'latitude',\n",
       "       u'longitude'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_store_info.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "air_reserve['visit_datetime'] = pd.to_datetime(air_reserve['visit_datetime'],format= '%Y-%m-%d %H:%M:%S')\n",
    "air_reserve['visit_date'] = air_reserve.visit_datetime.apply(lambda x: str(x).split(' ')[0])\n",
    "air_reserve['visit_date'] = pd.to_datetime(air_reserve['visit_date'],format= '%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hpg_reserve['visit_datetime'] = pd.to_datetime(hpg_reserve['visit_datetime'],format= '%Y-%m-%d %H:%M:%S')\n",
    "hpg_reserve['visit_date'] = hpg_reserve.visit_datetime.apply(lambda x: str(x).split(' ')[0])\n",
    "hpg_reserve['visit_date'] = pd.to_datetime(hpg_reserve['visit_date'],format= '%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reserve_datetime\n",
      "reserve_visitors\n"
     ]
    }
   ],
   "source": [
    "for i in ['reserve_datetime','reserve_visitors']:\n",
    "        k = air_reserve[[i,'visit_date','air_store_id']].groupby(['visit_date','air_store_id'])[i].apply(lambda x: x.tolist()).reset_index()\n",
    "        name = i + 'list'\n",
    "        if i == 'reserve_datetime':\n",
    "            k1  = k.copy()\n",
    "        else:\n",
    "            k1[name] = k[i].copy()\n",
    "        print i\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "air_visit_data = air_visit_data.merge(k1,on=['air_store_id','visit_date'],how = 'left')\n",
    "sample_submission = sample_submission.merge(k1,on=['air_store_id','visit_date'],how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "air_visit_data['visit_date_month'] =air_visit_data.visit_date.dt.month\n",
    "air_visit_data['visit_date_dayofw'] =air_visit_data.visit_date.dt.dayofweek\n",
    "air_visit_data['visit_date_year'] =air_visit_data.visit_date.dt.year\n",
    "air_visit_data['visit_date_dayofm'] =air_visit_data.visit_date.dt.day\n",
    "air_visit_data['weekofyear'] =air_visit_data.visit_date.dt.weekofyear\n",
    "sample_submission['visit_date_month'] =sample_submission.visit_date.dt.month\n",
    "sample_submission['visit_date_dayofw'] =sample_submission.visit_date.dt.dayofweek\n",
    "sample_submission['visit_date_year'] =sample_submission.visit_date.dt.year\n",
    "sample_submission['visit_date_dayofm'] =sample_submission.visit_date.dt.day\n",
    "sample_submission['weekofyear'] =sample_submission.visit_date.dt.weekofyear\n",
    "air_visit_data.loc[air_visit_data.weekofyear==53,'weekofyear'] =0 \n",
    "sample_submission.loc[sample_submission.weekofyear==53,'weekofyear'] =0 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "total_air_ids = list(air_store_info.air_store_id.unique())\n",
    "\n",
    "df_total = pd.concat((air_visit_data,sample_submission))\n",
    "df_total = df_total[['weekofyear','visit_date_year','air_store_id']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_total = df_total.sort_values(['visit_date_year','weekofyear'])\n",
    "weekofyear = list(df_total['weekofyear'].unique())\n",
    "year = list(df_total['visit_date_year'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "week_open_restro = []\n",
    "for i in year:\n",
    "    for j in weekofyear:\n",
    "        #print i,j,len(list(df_total.loc[(df_total.visit_date_year ==i) & (df_total.weekofyear ==j),'air_store_id'].unique()))\n",
    "        l= len(list(df_total.loc[(df_total.visit_date_year ==i) & (df_total.weekofyear ==j),'air_store_id'].unique()))\n",
    "        if (i==2017) & (j>22):\n",
    "            break\n",
    "        week_open_restro.append([i,j,l])\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_open_restro = pd.DataFrame(week_open_restro)\n",
    "df_open_restro.columns = ['visit_date_year','weekofyear','no_open_restro']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def func1(x):\n",
    "    try: \n",
    "        if pd.isnull(x):\n",
    "            return 0\n",
    "        else:\n",
    "            return len(x)\n",
    "                    \n",
    "    except:\n",
    "        return len(x)\n",
    "def func(x):\n",
    "    try: \n",
    "        if pd.isnull(x):\n",
    "            return -1\n",
    "        else:\n",
    "            return sum(x)                \n",
    "    except:\n",
    "        return sum(x)\n",
    "\n",
    "air_visit_data['total_reserve']= air_visit_data['reserve_visitorslist'].apply(func)\n",
    "air_visit_data['numb_total_reserve'] = air_visit_data['reserve_visitorslist'].apply(func1)\n",
    "\n",
    "sample_submission['total_reserve']= sample_submission['reserve_visitorslist'].apply(func)\n",
    "sample_submission['numb_total_reserve'] = sample_submission['reserve_visitorslist'].apply(func1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "k = [i for i in air_visit_data.columns if i in sample_submission.columns]\n",
    "train = air_visit_data.copy()\n",
    "test = sample_submission.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'air_store_id', u'visit_date', u'visitors', u'air_genre_name',\n",
       "       u'air_area_name', u'latitude', u'longitude', u'holiday_eve',\n",
       "       u'non_working', u'genre_in_area', u'total_r_in_area',\n",
       "       u'reserve_visitors', u'reserve_-12_h', u'reserve_12_37_h',\n",
       "       u'reserve_37_59_h', u'reserve_59_85_h', u'reserve_85+_h',\n",
       "       u'visitors_mean', u'visitors_median', u'visitors_max', u'visitors_min',\n",
       "       u'reserve_datetime', u'reserve_visitorslist', u'visit_date_month',\n",
       "       u'visit_date_dayofw', u'visit_date_year', u'visit_date_dayofm',\n",
       "       u'weekofyear', u'total_reserve', u'numb_total_reserve'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nl =  pd.to_datetime('2017-02-1',format='%Y-%m-%d')\\ntrain2 = train.loc[(train.visit_date>=l)]\""
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "l =  pd.to_datetime('2017-02-1',format='%Y-%m-%d')\n",
    "train2 = train.loc[(train.visit_date>=l)]\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2016-01-01 00:00:00'), Timestamp('2017-04-22 00:00:00'))"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.visit_date.min(),train.visit_date.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train1 = train.loc[(train.visit_date_year>=2017)].copy()\n",
    "k1 = train1[['visitors','air_store_id']].groupby('air_store_id').agg('mean').reset_index()\n",
    "k1.columns = ['air_store_id','mean_visitors']\n",
    "k2 = train1[['visitors','air_store_id']].groupby('air_store_id').agg('median').reset_index()\n",
    "k2.columns = ['air_store_id','median_visitors']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "k4 = train[['visitors','visit_date_month']].groupby(['visit_date_month']).agg('mean').reset_index()\n",
    "k4.columns = ['visit_date_month','mean_visitors2']\n",
    "\n",
    "k5 = train1[['visitors','air_store_id','visit_date_dayofw']].groupby(['air_store_id','visit_date_dayofw']).agg('mean').reset_index()\n",
    "k5.columns = ['air_store_id','visit_date_dayofw','xxx']\n",
    "\n",
    "k6 = train1[['visitors','visit_date_dayofw']].groupby(['visit_date_dayofw']).agg('mean').reset_index()\n",
    "k6.columns = ['visit_date_dayofw','mean_visitors4']\n",
    "\n",
    "\n",
    "k7 = train[['visitors','visit_date_month']].groupby(['visit_date_month']).agg('median').reset_index()\n",
    "k7.columns = ['visit_date_month','median_visitors2']\n",
    "\n",
    "k8 = train1[['visitors','air_store_id','visit_date_dayofw']].groupby(['air_store_id','visit_date_dayofw']).agg('median').reset_index()\n",
    "k8.columns = ['air_store_id','visit_date_dayofw','yyy']\n",
    "\n",
    "k9 = train1[['visitors','visit_date_dayofw']].groupby(['visit_date_dayofw']).agg('median').reset_index()\n",
    "k9.columns = ['visit_date_dayofw','median_visitors4']\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "total = pd.concat((train[['air_store_id','air_genre_name','air_area_name']],test[['air_store_id','air_genre_name','air_area_name']]))\n",
    "\n",
    "\n",
    "k10 = total.groupby(['air_genre_name']).agg('count')['air_store_id'].reset_index()\n",
    "k10.columns = ['air_genre_name','count1']\n",
    "k11 = total.groupby(['air_area_name']).agg('count')['air_store_id'].reset_index()\n",
    "k11.columns = ['air_area_name','count2']\n",
    "\n",
    "k12 = total.groupby(['air_store_id']).agg('count')['air_area_name'].reset_index()\n",
    "k12.columns = ['air_store_id','count2']\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "k10 = train[['visitors','air_store_id']].groupby('air_store_id').agg('mean').reset_index()\n",
    "k10.columns = ['air_store_id','mean_visitors_f']\n",
    "k11 = train[['visitors','air_store_id','visit_date_dayofw']].groupby(['air_store_id','visit_date_dayofw']).agg('mean').reset_index()\n",
    "k11.columns = ['air_store_id','visit_date_dayofw','mean_visitors3_f']\n",
    "k12 = train[['visitors','visit_date_dayofw']].groupby(['visit_date_dayofw']).agg('mean').reset_index()\n",
    "k12.columns = ['visit_date_dayofw','mean_visitors4_f']\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "k13 = train[['visitors','weekofyear']].groupby(['weekofyear']).agg('mean').reset_index()\n",
    "k13.columns = ['weekofyear','mean_visitors2_weekofyear']\n",
    "k14 = train[['visitors','weekofyear']].groupby(['weekofyear']).agg('median').reset_index()\n",
    "k14.columns = ['weekofyear','median_visitors2_weekofyear']\n",
    "\n",
    "\n",
    "\n",
    "k15 = train[['visitors','air_store_id','visit_date_month']].groupby(['air_store_id','visit_date_month']).agg('mean').reset_index()\n",
    "\n",
    "\n",
    "#k12 = train[['visitors','air_genre_name','air_area_name']].groupby(['air_genre_name','air_area_name']).agg('mean').reset_index()\n",
    "#k12.columns = ['air_genre_name','air_area_name','mean_air_area_name_visitor']\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nk15.columns = ['air_store_id','visit_date_month','xxx']\\ntrain = train.merge(k15,on=['air_store_id','visit_date_month'],how='left')\\ntest = test.merge(k15,on= ['air_store_id','visit_date_month'],how='left')\\nk1.columns = ['air_store_id', 'yyy']\\ntest= test.merge(k1,on = 'air_store_id',how = 'left')\\ntest.loc[pd.isnull(test.xxx),'xxx'] = test.loc[pd.isnull(test.xxx),'yyy'].copy()\\ntest = test.drop('yyy',axis=1)\\n\""
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "k15.columns = ['air_store_id','visit_date_month','xxx']\n",
    "train = train.merge(k15,on=['air_store_id','visit_date_month'],how='left')\n",
    "test = test.merge(k15,on= ['air_store_id','visit_date_month'],how='left')\n",
    "k1.columns = ['air_store_id', 'yyy']\n",
    "test= test.merge(k1,on = 'air_store_id',how = 'left')\n",
    "test.loc[pd.isnull(test.xxx),'xxx'] = test.loc[pd.isnull(test.xxx),'yyy'].copy()\n",
    "test = test.drop('yyy',axis=1)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = air_visit_data.copy()\n",
    "test = sample_submission.copy()\n",
    "train = train[k]\n",
    "test = test[k]\n",
    "\n",
    "\n",
    "\n",
    "train = train.merge(k1,on='air_store_id',how='left')\n",
    "test = test.merge(k1,on='air_store_id',how='left')\n",
    "train = train.merge(k2,on='air_store_id',how='left')\n",
    "test = test.merge(k2,on='air_store_id',how='left')\n",
    "#train = train.merge(k3,on=['air_store_id','visit_date_month'],how='left')\n",
    "#test = test.merge(k3,on= ['air_store_id','visit_date_month'],how='left')\n",
    "\n",
    "train = train.merge(k4,on=['visit_date_month'],how='left')\n",
    "test = test.merge(k4,on= ['visit_date_month'],how='left')\n",
    "#train = train.merge(k5,on=['air_store_id','visit_date_dayofw'],how='left')\n",
    "#test = test.merge(k5,on= ['air_store_id','visit_date_dayofw'],how='left')\n",
    "train = train.merge(k6,on=['visit_date_dayofw'],how='left')\n",
    "test = test.merge(k6,on= ['visit_date_dayofw'],how='left')\n",
    "\n",
    "\n",
    "train = train.merge(k7,on=['visit_date_month'],how='left')\n",
    "test = test.merge(k7,on= ['visit_date_month'],how='left')\n",
    "#train = train.merge(k8,on=['air_store_id','visit_date_dayofw'],how='left')\n",
    "#test = test.merge(k8,on= ['air_store_id','visit_date_dayofw'],how='left')\n",
    "train = train.merge(k9,on=['visit_date_dayofw'],how='left')\n",
    "test = test.merge(k9,on= ['visit_date_dayofw'],how='left')\n",
    "\n",
    "train = train.merge(k10,on=['air_store_id'],how='left')\n",
    "test = test.merge(k10,on= ['air_store_id'],how='left')\n",
    "train = train.merge(k11,on=['air_store_id','visit_date_dayofw'],how='left')\n",
    "test = test.merge(k11,on= ['air_store_id','visit_date_dayofw'],how='left')\n",
    "train = train.merge(k12,on=['visit_date_dayofw'],how='left')\n",
    "test = test.merge(k12,on= ['visit_date_dayofw'],how='left')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "train = train.merge(df_open_restro,on=['visit_date_year','weekofyear'],how='left')\n",
    "test = test.merge(df_open_restro,on= ['visit_date_year','weekofyear'],how='left')\n",
    "\n",
    "\n",
    "\n",
    "#train = train.merge(k13,on=['weekofyear'],how='left')\n",
    "#test = test.merge(k13,on= ['weekofyear'],how='left')\n",
    "#train = train.merge(k14,on=['weekofyear'],how='left')\n",
    "#test = test.merge(k14,on= ['weekofyear'],how='left')\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "n1 = air_store_info.groupby(['air_genre_name','air_area_name']).agg('count')['air_store_id'].reset_index()\n",
    "n1.columns = ['air_genre_name','air_area_name','air_genre_name_c']\n",
    "\n",
    "\n",
    "n2 = n1.groupby('air_genre_name').agg('count')['air_area_name'].reset_index()\n",
    "n2.columns = ['air_genre_name','air_area_name_unique']\n",
    "\n",
    "n3 = n1.groupby('air_area_name').agg('count')['air_genre_name'].reset_index()\n",
    "n3.columns = ['air_area_name','air_genre_name_unique']\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "train = train.merge(k14,on=['air_store_id','visit_date_dayofw'],how='left')\n",
    "test = test.merge(k14,on= ['air_store_id','visit_date_dayofw'],how='left')\n",
    "train = train.merge(k15,on=['visit_date_dayofw'],how='left')\n",
    "test = test.merge(k15,on= ['visit_date_dayofw'],how='left')\n",
    "\"\"\"\n",
    "y = train.visitors.values\n",
    "ids = sample_submission['id'].values\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test.loc[pd.isnull(test.xxx),['yyy','xxx']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "holidays at weekends are not special, right?\n"
     ]
    }
   ],
   "source": [
    "\n",
    "date_info = pd.read_csv('date_info.csv')\n",
    "date_info['calendar_date']  = pd.to_datetime(date_info['calendar_date'],format= '%Y-%m-%d')\n",
    "date_info.rename(columns = {'calendar_date':'visit_date'},inplace = True)\n",
    "\n",
    "print('holidays at weekends are not special, right?')\n",
    "wkend_holidays = date_info.apply((lambda x:(x.day_of_week=='Sunday' or x.day_of_week=='Saturday') and x.holiday_flg==1), axis=1)\n",
    "date_info.loc[wkend_holidays, 'holiday_flg'] = 0\n",
    "date_info['weight'] = ((date_info.index + 1.0) / len(date_info)) ** 5.0\n",
    "\n",
    "\n",
    "train = train.merge(date_info,on='visit_date',how='left')\n",
    "test = test.merge(date_info,on='visit_date',how='left')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nk1 = train.loc[train.holiday_flg == 1].groupby(['air_store_id','visit_date_dayofw'])['visitors'].agg('mean').reset_index()\\nk2 = train.loc[train.holiday_flg != 1].groupby(['air_store_id','visit_date_dayofw'])['visitors'].agg('mean').reset_index()\\nk1.columns = ['air_store_id','visit_date_dayofw','holiday_flg_1']\\nk2.columns = ['air_store_id','visit_date_dayofw','holiday_flg_1']\\n\\nk = k1.merge(k2,on=['air_store_id','visit_date_dayofw'],how='left')\\ntrain = train.merge(k,how='left',on=['air_store_id','visit_date_dayofw'])\\ntest = test.merge(k,how='left',on=['air_store_id','visit_date_dayofw'])\\nk1 = train.loc[train.holiday_flg == 1].groupby(['air_area_name','visit_date_dayofw'])['visitors'].agg('mean').reset_index()\\nk2 = train.loc[train.holiday_flg != 1].groupby(['air_area_name','visit_date_dayofw'])['visitors'].agg('mean').reset_index()\\nk1.columns = ['air_area_name','visit_date_dayofw','holiday_flg_1']\\nk2.columns = ['air_area_name','visit_date_dayofw','holiday_flg_1']\\n\\nk = k1.merge(k2,on=['air_area_name','visit_date_dayofw'],how='left')\\ntrain = train.merge(k,how='left',on=['air_area_name','visit_date_dayofw'])\\ntest = test.merge(k,how='left',on=['air_area_name','visit_date_dayofw'])\\n\""
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "k1 = train.loc[train.holiday_flg == 1].groupby(['air_store_id','visit_date_dayofw'])['visitors'].agg('mean').reset_index()\n",
    "k2 = train.loc[train.holiday_flg != 1].groupby(['air_store_id','visit_date_dayofw'])['visitors'].agg('mean').reset_index()\n",
    "k1.columns = ['air_store_id','visit_date_dayofw','holiday_flg_1']\n",
    "k2.columns = ['air_store_id','visit_date_dayofw','holiday_flg_1']\n",
    "\n",
    "k = k1.merge(k2,on=['air_store_id','visit_date_dayofw'],how='left')\n",
    "train = train.merge(k,how='left',on=['air_store_id','visit_date_dayofw'])\n",
    "test = test.merge(k,how='left',on=['air_store_id','visit_date_dayofw'])\n",
    "k1 = train.loc[train.holiday_flg == 1].groupby(['air_area_name','visit_date_dayofw'])['visitors'].agg('mean').reset_index()\n",
    "k2 = train.loc[train.holiday_flg != 1].groupby(['air_area_name','visit_date_dayofw'])['visitors'].agg('mean').reset_index()\n",
    "k1.columns = ['air_area_name','visit_date_dayofw','holiday_flg_1']\n",
    "k2.columns = ['air_area_name','visit_date_dayofw','holiday_flg_1']\n",
    "\n",
    "k = k1.merge(k2,on=['air_area_name','visit_date_dayofw'],how='left')\n",
    "train = train.merge(k,how='left',on=['air_area_name','visit_date_dayofw'])\n",
    "test = test.merge(k,how='left',on=['air_area_name','visit_date_dayofw'])\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "relation = pd.read_csv('store_id_relation.csv')\n",
    "relation['both'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\no = hpg_reserve[['hpg_store_id','visit_date']]\\no = o.merge(relation,on='hpg_store_id',how='left')\\no  = o.loc[pd.notnull(o.air_store_id)]\\no = o[['air_store_id','visit_date']]\\np = air_reserve[['air_store_id','visit_date']]\\nq = pd.concat((o,p),axis=0)\\nd =q.groupby(['air_store_id', u'visit_date']).agg('count').reset_index()[['air_store_id','visit_date']]\\nd = d.merge(air_store_info,how='left',on='air_store_id')\\nd = d[['air_store_id','visit_date','air_area_name']]\\nl = d.groupby('visit_date').agg('count')['air_store_id'].reset_index()\\nl.columns = ['visit_date','restoopend']\\nm = air_store_info.groupby('air_area_name').agg('count')['air_store_id'].reset_index()\\nm.columns = ['air_area_name','restro_in_that_area']\\nair_visit_data = air_visit_data.merge(l, on = 'visit_date', how= 'left')\\nair_visit_data = air_visit_data.merge(m, on = 'air_area_name', how= 'left')\\nsample_submission = sample_submission.merge(l, on = 'visit_date', how= 'left')\\nsample_submission = sample_submission.merge(m, on = 'air_area_name', how= 'left')\\nn =d.groupby(['visit_date','air_area_name']).agg('count')['air_store_id'].reset_index()\\nn.columns = ['visit_date','air_area_name','date_are_restro_opened']\\nair_visit_data = air_visit_data.merge(n, on = ['visit_date','air_area_name'], how= 'left')\\nsample_submission = sample_submission.merge(n, on = ['visit_date','air_area_name'], how= 'left')\\n\""
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "o = hpg_reserve[['hpg_store_id','visit_date']]\n",
    "o = o.merge(relation,on='hpg_store_id',how='left')\n",
    "o  = o.loc[pd.notnull(o.air_store_id)]\n",
    "o = o[['air_store_id','visit_date']]\n",
    "p = air_reserve[['air_store_id','visit_date']]\n",
    "q = pd.concat((o,p),axis=0)\n",
    "d =q.groupby(['air_store_id', u'visit_date']).agg('count').reset_index()[['air_store_id','visit_date']]\n",
    "d = d.merge(air_store_info,how='left',on='air_store_id')\n",
    "d = d[['air_store_id','visit_date','air_area_name']]\n",
    "l = d.groupby('visit_date').agg('count')['air_store_id'].reset_index()\n",
    "l.columns = ['visit_date','restoopend']\n",
    "m = air_store_info.groupby('air_area_name').agg('count')['air_store_id'].reset_index()\n",
    "m.columns = ['air_area_name','restro_in_that_area']\n",
    "air_visit_data = air_visit_data.merge(l, on = 'visit_date', how= 'left')\n",
    "air_visit_data = air_visit_data.merge(m, on = 'air_area_name', how= 'left')\n",
    "sample_submission = sample_submission.merge(l, on = 'visit_date', how= 'left')\n",
    "sample_submission = sample_submission.merge(m, on = 'air_area_name', how= 'left')\n",
    "n =d.groupby(['visit_date','air_area_name']).agg('count')['air_store_id'].reset_index()\n",
    "n.columns = ['visit_date','air_area_name','date_are_restro_opened']\n",
    "air_visit_data = air_visit_data.merge(n, on = ['visit_date','air_area_name'], how= 'left')\n",
    "sample_submission = sample_submission.merge(n, on = ['visit_date','air_area_name'], how= 'left')\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>visitors</th>\n",
       "      <th>air_store_id</th>\n",
       "      <th>visit_date</th>\n",
       "      <th>air_genre_name</th>\n",
       "      <th>air_area_name</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>holiday_eve</th>\n",
       "      <th>non_working</th>\n",
       "      <th>...</th>\n",
       "      <th>visitors_min</th>\n",
       "      <th>reserve_datetime</th>\n",
       "      <th>reserve_visitorslist</th>\n",
       "      <th>visit_date_month</th>\n",
       "      <th>visit_date_dayofw</th>\n",
       "      <th>visit_date_year</th>\n",
       "      <th>visit_date_dayofm</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>total_reserve</th>\n",
       "      <th>numb_total_reserve</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-04-23</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-04-23</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-04-24</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-04-24</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>24</td>\n",
       "      <td>17</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-04-25</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-04-25</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>25</td>\n",
       "      <td>17</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-04-26</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-04-26</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>26</td>\n",
       "      <td>17</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-04-27</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-04-27</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>27</td>\n",
       "      <td>17</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-04-28</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>28</td>\n",
       "      <td>17</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-04-29</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-04-29</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>29</td>\n",
       "      <td>17</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-04-30</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-04-30</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-05-01</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-05-02</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-05-02</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-05-03</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-05-03</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-05-04</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-05-04</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-05-05</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-05-05</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-05-06</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-05-06</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-05-07</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-05-07</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-05-08</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-05-08</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-05-09</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-05-09</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-05-10</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-05-10</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-05-11</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-05-12</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-05-12</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-05-13</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-05-13</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>13</td>\n",
       "      <td>19</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-05-14</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-05-14</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-05-15</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-05-15</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-05-16</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-05-16</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-05-17</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-05-17</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-05-18</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-05-18</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-05-19</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-05-19</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-05-20</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-05-20</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-05-21</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-05-21</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>air_00a91d42b08b08d9_2017-05-22</td>\n",
       "      <td>0</td>\n",
       "      <td>air_00a91d42b08b08d9</td>\n",
       "      <td>2017-05-22</td>\n",
       "      <td>Italian/French</td>\n",
       "      <td>Tōkyō-to Chiyoda-ku Kudanminami</td>\n",
       "      <td>35.694003</td>\n",
       "      <td>139.753595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31989</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-02</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-02</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31990</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-03</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-03</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31991</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-04</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-04</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31992</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-05</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-05</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31993</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-06</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-06</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31994</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-07</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-07</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31995</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-08</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-08</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31996</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-09</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-09</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31997</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-10</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-10</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31998</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-11</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31999</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-12</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-12</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32000</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-13</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-13</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>13</td>\n",
       "      <td>19</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32001</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-14</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-14</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32002</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-15</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-15</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32003</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-16</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-16</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32004</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-17</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-17</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32005</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-18</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-18</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32006</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-19</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-19</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32007</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-20</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-20</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32008</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-21</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-21</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32009</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-22</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-22</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32010</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-23</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-23</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32011</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-24</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-24</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32012</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-25</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-25</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>25</td>\n",
       "      <td>21</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32013</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-26</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>26</td>\n",
       "      <td>21</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32014</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-27</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-27</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>27</td>\n",
       "      <td>21</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32015</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-28</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-28</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>28</td>\n",
       "      <td>21</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32016</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-29</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-29</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>29</td>\n",
       "      <td>22</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32017</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-30</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-30</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32018</th>\n",
       "      <td>air_fff68b929994bfbd_2017-05-31</td>\n",
       "      <td>0</td>\n",
       "      <td>air_fff68b929994bfbd</td>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>Bar/Cocktail</td>\n",
       "      <td>Tōkyō-to Nakano-ku Nakano</td>\n",
       "      <td>35.708146</td>\n",
       "      <td>139.666288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32019 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    id  visitors          air_store_id  \\\n",
       "0      air_00a91d42b08b08d9_2017-04-23         0  air_00a91d42b08b08d9   \n",
       "1      air_00a91d42b08b08d9_2017-04-24         0  air_00a91d42b08b08d9   \n",
       "2      air_00a91d42b08b08d9_2017-04-25         0  air_00a91d42b08b08d9   \n",
       "3      air_00a91d42b08b08d9_2017-04-26         0  air_00a91d42b08b08d9   \n",
       "4      air_00a91d42b08b08d9_2017-04-27         0  air_00a91d42b08b08d9   \n",
       "5      air_00a91d42b08b08d9_2017-04-28         0  air_00a91d42b08b08d9   \n",
       "6      air_00a91d42b08b08d9_2017-04-29         0  air_00a91d42b08b08d9   \n",
       "7      air_00a91d42b08b08d9_2017-04-30         0  air_00a91d42b08b08d9   \n",
       "8      air_00a91d42b08b08d9_2017-05-01         0  air_00a91d42b08b08d9   \n",
       "9      air_00a91d42b08b08d9_2017-05-02         0  air_00a91d42b08b08d9   \n",
       "10     air_00a91d42b08b08d9_2017-05-03         0  air_00a91d42b08b08d9   \n",
       "11     air_00a91d42b08b08d9_2017-05-04         0  air_00a91d42b08b08d9   \n",
       "12     air_00a91d42b08b08d9_2017-05-05         0  air_00a91d42b08b08d9   \n",
       "13     air_00a91d42b08b08d9_2017-05-06         0  air_00a91d42b08b08d9   \n",
       "14     air_00a91d42b08b08d9_2017-05-07         0  air_00a91d42b08b08d9   \n",
       "15     air_00a91d42b08b08d9_2017-05-08         0  air_00a91d42b08b08d9   \n",
       "16     air_00a91d42b08b08d9_2017-05-09         0  air_00a91d42b08b08d9   \n",
       "17     air_00a91d42b08b08d9_2017-05-10         0  air_00a91d42b08b08d9   \n",
       "18     air_00a91d42b08b08d9_2017-05-11         0  air_00a91d42b08b08d9   \n",
       "19     air_00a91d42b08b08d9_2017-05-12         0  air_00a91d42b08b08d9   \n",
       "20     air_00a91d42b08b08d9_2017-05-13         0  air_00a91d42b08b08d9   \n",
       "21     air_00a91d42b08b08d9_2017-05-14         0  air_00a91d42b08b08d9   \n",
       "22     air_00a91d42b08b08d9_2017-05-15         0  air_00a91d42b08b08d9   \n",
       "23     air_00a91d42b08b08d9_2017-05-16         0  air_00a91d42b08b08d9   \n",
       "24     air_00a91d42b08b08d9_2017-05-17         0  air_00a91d42b08b08d9   \n",
       "25     air_00a91d42b08b08d9_2017-05-18         0  air_00a91d42b08b08d9   \n",
       "26     air_00a91d42b08b08d9_2017-05-19         0  air_00a91d42b08b08d9   \n",
       "27     air_00a91d42b08b08d9_2017-05-20         0  air_00a91d42b08b08d9   \n",
       "28     air_00a91d42b08b08d9_2017-05-21         0  air_00a91d42b08b08d9   \n",
       "29     air_00a91d42b08b08d9_2017-05-22         0  air_00a91d42b08b08d9   \n",
       "...                                ...       ...                   ...   \n",
       "31989  air_fff68b929994bfbd_2017-05-02         0  air_fff68b929994bfbd   \n",
       "31990  air_fff68b929994bfbd_2017-05-03         0  air_fff68b929994bfbd   \n",
       "31991  air_fff68b929994bfbd_2017-05-04         0  air_fff68b929994bfbd   \n",
       "31992  air_fff68b929994bfbd_2017-05-05         0  air_fff68b929994bfbd   \n",
       "31993  air_fff68b929994bfbd_2017-05-06         0  air_fff68b929994bfbd   \n",
       "31994  air_fff68b929994bfbd_2017-05-07         0  air_fff68b929994bfbd   \n",
       "31995  air_fff68b929994bfbd_2017-05-08         0  air_fff68b929994bfbd   \n",
       "31996  air_fff68b929994bfbd_2017-05-09         0  air_fff68b929994bfbd   \n",
       "31997  air_fff68b929994bfbd_2017-05-10         0  air_fff68b929994bfbd   \n",
       "31998  air_fff68b929994bfbd_2017-05-11         0  air_fff68b929994bfbd   \n",
       "31999  air_fff68b929994bfbd_2017-05-12         0  air_fff68b929994bfbd   \n",
       "32000  air_fff68b929994bfbd_2017-05-13         0  air_fff68b929994bfbd   \n",
       "32001  air_fff68b929994bfbd_2017-05-14         0  air_fff68b929994bfbd   \n",
       "32002  air_fff68b929994bfbd_2017-05-15         0  air_fff68b929994bfbd   \n",
       "32003  air_fff68b929994bfbd_2017-05-16         0  air_fff68b929994bfbd   \n",
       "32004  air_fff68b929994bfbd_2017-05-17         0  air_fff68b929994bfbd   \n",
       "32005  air_fff68b929994bfbd_2017-05-18         0  air_fff68b929994bfbd   \n",
       "32006  air_fff68b929994bfbd_2017-05-19         0  air_fff68b929994bfbd   \n",
       "32007  air_fff68b929994bfbd_2017-05-20         0  air_fff68b929994bfbd   \n",
       "32008  air_fff68b929994bfbd_2017-05-21         0  air_fff68b929994bfbd   \n",
       "32009  air_fff68b929994bfbd_2017-05-22         0  air_fff68b929994bfbd   \n",
       "32010  air_fff68b929994bfbd_2017-05-23         0  air_fff68b929994bfbd   \n",
       "32011  air_fff68b929994bfbd_2017-05-24         0  air_fff68b929994bfbd   \n",
       "32012  air_fff68b929994bfbd_2017-05-25         0  air_fff68b929994bfbd   \n",
       "32013  air_fff68b929994bfbd_2017-05-26         0  air_fff68b929994bfbd   \n",
       "32014  air_fff68b929994bfbd_2017-05-27         0  air_fff68b929994bfbd   \n",
       "32015  air_fff68b929994bfbd_2017-05-28         0  air_fff68b929994bfbd   \n",
       "32016  air_fff68b929994bfbd_2017-05-29         0  air_fff68b929994bfbd   \n",
       "32017  air_fff68b929994bfbd_2017-05-30         0  air_fff68b929994bfbd   \n",
       "32018  air_fff68b929994bfbd_2017-05-31         0  air_fff68b929994bfbd   \n",
       "\n",
       "      visit_date  air_genre_name                    air_area_name   latitude  \\\n",
       "0     2017-04-23  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "1     2017-04-24  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "2     2017-04-25  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "3     2017-04-26  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "4     2017-04-27  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "5     2017-04-28  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "6     2017-04-29  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "7     2017-04-30  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "8     2017-05-01  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "9     2017-05-02  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "10    2017-05-03  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "11    2017-05-04  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "12    2017-05-05  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "13    2017-05-06  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "14    2017-05-07  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "15    2017-05-08  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "16    2017-05-09  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "17    2017-05-10  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "18    2017-05-11  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "19    2017-05-12  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "20    2017-05-13  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "21    2017-05-14  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "22    2017-05-15  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "23    2017-05-16  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "24    2017-05-17  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "25    2017-05-18  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "26    2017-05-19  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "27    2017-05-20  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "28    2017-05-21  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "29    2017-05-22  Italian/French  Tōkyō-to Chiyoda-ku Kudanminami  35.694003   \n",
       "...          ...             ...                              ...        ...   \n",
       "31989 2017-05-02    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "31990 2017-05-03    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "31991 2017-05-04    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "31992 2017-05-05    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "31993 2017-05-06    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "31994 2017-05-07    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "31995 2017-05-08    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "31996 2017-05-09    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "31997 2017-05-10    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "31998 2017-05-11    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "31999 2017-05-12    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "32000 2017-05-13    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "32001 2017-05-14    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "32002 2017-05-15    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "32003 2017-05-16    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "32004 2017-05-17    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "32005 2017-05-18    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "32006 2017-05-19    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "32007 2017-05-20    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "32008 2017-05-21    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "32009 2017-05-22    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "32010 2017-05-23    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "32011 2017-05-24    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "32012 2017-05-25    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "32013 2017-05-26    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "32014 2017-05-27    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "32015 2017-05-28    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "32016 2017-05-29    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "32017 2017-05-30    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "32018 2017-05-31    Bar/Cocktail        Tōkyō-to Nakano-ku Nakano  35.708146   \n",
       "\n",
       "        longitude  holiday_eve  non_working         ...          visitors_min  \\\n",
       "0      139.753595          0.0            1         ...              1.098612   \n",
       "1      139.753595          0.0            0         ...              0.693147   \n",
       "2      139.753595          0.0            0         ...              0.693147   \n",
       "3      139.753595          0.0            0         ...              0.693147   \n",
       "4      139.753595          0.0            0         ...              0.693147   \n",
       "5      139.753595          1.0            0         ...              0.693147   \n",
       "6      139.753595          0.0            1         ...              1.098612   \n",
       "7      139.753595          0.0            1         ...              1.098612   \n",
       "8      139.753595          0.0            0         ...              0.693147   \n",
       "9      139.753595          1.0            0         ...              0.693147   \n",
       "10     139.753595          1.0            1         ...              1.098612   \n",
       "11     139.753595          1.0            1         ...              1.098612   \n",
       "12     139.753595          0.0            1         ...              1.098612   \n",
       "13     139.753595          0.0            1         ...              1.098612   \n",
       "14     139.753595          0.0            1         ...              1.098612   \n",
       "15     139.753595          0.0            0         ...              0.693147   \n",
       "16     139.753595          0.0            0         ...              0.693147   \n",
       "17     139.753595          0.0            0         ...              0.693147   \n",
       "18     139.753595          0.0            0         ...              0.693147   \n",
       "19     139.753595          0.0            0         ...              0.693147   \n",
       "20     139.753595          0.0            1         ...              1.098612   \n",
       "21     139.753595          0.0            1         ...              1.098612   \n",
       "22     139.753595          0.0            0         ...              0.693147   \n",
       "23     139.753595          0.0            0         ...              0.693147   \n",
       "24     139.753595          0.0            0         ...              0.693147   \n",
       "25     139.753595          0.0            0         ...              0.693147   \n",
       "26     139.753595          0.0            0         ...              0.693147   \n",
       "27     139.753595          0.0            1         ...              1.098612   \n",
       "28     139.753595          0.0            1         ...              1.098612   \n",
       "29     139.753595          0.0            0         ...              0.693147   \n",
       "...           ...          ...          ...         ...                   ...   \n",
       "31989  139.666288          1.0            0         ...              0.693147   \n",
       "31990  139.666288          1.0            1         ...              0.693147   \n",
       "31991  139.666288          1.0            1         ...              0.693147   \n",
       "31992  139.666288          0.0            1         ...              0.693147   \n",
       "31993  139.666288          0.0            1         ...              0.693147   \n",
       "31994  139.666288          0.0            1         ...              0.693147   \n",
       "31995  139.666288          0.0            0         ...              0.693147   \n",
       "31996  139.666288          0.0            0         ...              0.693147   \n",
       "31997  139.666288          0.0            0         ...              0.693147   \n",
       "31998  139.666288          0.0            0         ...              0.693147   \n",
       "31999  139.666288          0.0            0         ...              0.693147   \n",
       "32000  139.666288          0.0            1         ...              0.693147   \n",
       "32001  139.666288          0.0            1         ...              0.693147   \n",
       "32002  139.666288          0.0            0         ...              0.693147   \n",
       "32003  139.666288          0.0            0         ...              0.693147   \n",
       "32004  139.666288          0.0            0         ...              0.693147   \n",
       "32005  139.666288          0.0            0         ...              0.693147   \n",
       "32006  139.666288          0.0            0         ...              0.693147   \n",
       "32007  139.666288          0.0            1         ...              0.693147   \n",
       "32008  139.666288          0.0            1         ...              0.693147   \n",
       "32009  139.666288          0.0            0         ...              0.693147   \n",
       "32010  139.666288          0.0            0         ...              0.693147   \n",
       "32011  139.666288          0.0            0         ...              0.693147   \n",
       "32012  139.666288          0.0            0         ...              0.693147   \n",
       "32013  139.666288          0.0            0         ...              0.693147   \n",
       "32014  139.666288          0.0            1         ...              0.693147   \n",
       "32015  139.666288          0.0            1         ...              0.693147   \n",
       "32016  139.666288          0.0            0         ...              0.693147   \n",
       "32017  139.666288          0.0            0         ...              0.693147   \n",
       "32018  139.666288          0.0            0         ...              0.693147   \n",
       "\n",
       "       reserve_datetime  reserve_visitorslist  visit_date_month  \\\n",
       "0                   NaN                   NaN                 4   \n",
       "1                   NaN                   NaN                 4   \n",
       "2                   NaN                   NaN                 4   \n",
       "3                   NaN                   NaN                 4   \n",
       "4                   NaN                   NaN                 4   \n",
       "5                   NaN                   NaN                 4   \n",
       "6                   NaN                   NaN                 4   \n",
       "7                   NaN                   NaN                 4   \n",
       "8                   NaN                   NaN                 5   \n",
       "9                   NaN                   NaN                 5   \n",
       "10                  NaN                   NaN                 5   \n",
       "11                  NaN                   NaN                 5   \n",
       "12                  NaN                   NaN                 5   \n",
       "13                  NaN                   NaN                 5   \n",
       "14                  NaN                   NaN                 5   \n",
       "15                  NaN                   NaN                 5   \n",
       "16                  NaN                   NaN                 5   \n",
       "17                  NaN                   NaN                 5   \n",
       "18                  NaN                   NaN                 5   \n",
       "19                  NaN                   NaN                 5   \n",
       "20                  NaN                   NaN                 5   \n",
       "21                  NaN                   NaN                 5   \n",
       "22                  NaN                   NaN                 5   \n",
       "23                  NaN                   NaN                 5   \n",
       "24                  NaN                   NaN                 5   \n",
       "25                  NaN                   NaN                 5   \n",
       "26                  NaN                   NaN                 5   \n",
       "27                  NaN                   NaN                 5   \n",
       "28                  NaN                   NaN                 5   \n",
       "29                  NaN                   NaN                 5   \n",
       "...                 ...                   ...               ...   \n",
       "31989               NaN                   NaN                 5   \n",
       "31990               NaN                   NaN                 5   \n",
       "31991               NaN                   NaN                 5   \n",
       "31992               NaN                   NaN                 5   \n",
       "31993               NaN                   NaN                 5   \n",
       "31994               NaN                   NaN                 5   \n",
       "31995               NaN                   NaN                 5   \n",
       "31996               NaN                   NaN                 5   \n",
       "31997               NaN                   NaN                 5   \n",
       "31998               NaN                   NaN                 5   \n",
       "31999               NaN                   NaN                 5   \n",
       "32000               NaN                   NaN                 5   \n",
       "32001               NaN                   NaN                 5   \n",
       "32002               NaN                   NaN                 5   \n",
       "32003               NaN                   NaN                 5   \n",
       "32004               NaN                   NaN                 5   \n",
       "32005               NaN                   NaN                 5   \n",
       "32006               NaN                   NaN                 5   \n",
       "32007               NaN                   NaN                 5   \n",
       "32008               NaN                   NaN                 5   \n",
       "32009               NaN                   NaN                 5   \n",
       "32010               NaN                   NaN                 5   \n",
       "32011               NaN                   NaN                 5   \n",
       "32012               NaN                   NaN                 5   \n",
       "32013               NaN                   NaN                 5   \n",
       "32014               NaN                   NaN                 5   \n",
       "32015               NaN                   NaN                 5   \n",
       "32016               NaN                   NaN                 5   \n",
       "32017               NaN                   NaN                 5   \n",
       "32018               NaN                   NaN                 5   \n",
       "\n",
       "       visit_date_dayofw  visit_date_year  visit_date_dayofm  weekofyear  \\\n",
       "0                      6             2017                 23          16   \n",
       "1                      0             2017                 24          17   \n",
       "2                      1             2017                 25          17   \n",
       "3                      2             2017                 26          17   \n",
       "4                      3             2017                 27          17   \n",
       "5                      4             2017                 28          17   \n",
       "6                      5             2017                 29          17   \n",
       "7                      6             2017                 30          17   \n",
       "8                      0             2017                  1          18   \n",
       "9                      1             2017                  2          18   \n",
       "10                     2             2017                  3          18   \n",
       "11                     3             2017                  4          18   \n",
       "12                     4             2017                  5          18   \n",
       "13                     5             2017                  6          18   \n",
       "14                     6             2017                  7          18   \n",
       "15                     0             2017                  8          19   \n",
       "16                     1             2017                  9          19   \n",
       "17                     2             2017                 10          19   \n",
       "18                     3             2017                 11          19   \n",
       "19                     4             2017                 12          19   \n",
       "20                     5             2017                 13          19   \n",
       "21                     6             2017                 14          19   \n",
       "22                     0             2017                 15          20   \n",
       "23                     1             2017                 16          20   \n",
       "24                     2             2017                 17          20   \n",
       "25                     3             2017                 18          20   \n",
       "26                     4             2017                 19          20   \n",
       "27                     5             2017                 20          20   \n",
       "28                     6             2017                 21          20   \n",
       "29                     0             2017                 22          21   \n",
       "...                  ...              ...                ...         ...   \n",
       "31989                  1             2017                  2          18   \n",
       "31990                  2             2017                  3          18   \n",
       "31991                  3             2017                  4          18   \n",
       "31992                  4             2017                  5          18   \n",
       "31993                  5             2017                  6          18   \n",
       "31994                  6             2017                  7          18   \n",
       "31995                  0             2017                  8          19   \n",
       "31996                  1             2017                  9          19   \n",
       "31997                  2             2017                 10          19   \n",
       "31998                  3             2017                 11          19   \n",
       "31999                  4             2017                 12          19   \n",
       "32000                  5             2017                 13          19   \n",
       "32001                  6             2017                 14          19   \n",
       "32002                  0             2017                 15          20   \n",
       "32003                  1             2017                 16          20   \n",
       "32004                  2             2017                 17          20   \n",
       "32005                  3             2017                 18          20   \n",
       "32006                  4             2017                 19          20   \n",
       "32007                  5             2017                 20          20   \n",
       "32008                  6             2017                 21          20   \n",
       "32009                  0             2017                 22          21   \n",
       "32010                  1             2017                 23          21   \n",
       "32011                  2             2017                 24          21   \n",
       "32012                  3             2017                 25          21   \n",
       "32013                  4             2017                 26          21   \n",
       "32014                  5             2017                 27          21   \n",
       "32015                  6             2017                 28          21   \n",
       "32016                  0             2017                 29          22   \n",
       "32017                  1             2017                 30          22   \n",
       "32018                  2             2017                 31          22   \n",
       "\n",
       "       total_reserve  numb_total_reserve  \n",
       "0                 -1                   0  \n",
       "1                 -1                   0  \n",
       "2                 -1                   0  \n",
       "3                 -1                   0  \n",
       "4                 -1                   0  \n",
       "5                 -1                   0  \n",
       "6                 -1                   0  \n",
       "7                 -1                   0  \n",
       "8                 -1                   0  \n",
       "9                 -1                   0  \n",
       "10                -1                   0  \n",
       "11                -1                   0  \n",
       "12                -1                   0  \n",
       "13                -1                   0  \n",
       "14                -1                   0  \n",
       "15                -1                   0  \n",
       "16                -1                   0  \n",
       "17                -1                   0  \n",
       "18                -1                   0  \n",
       "19                -1                   0  \n",
       "20                -1                   0  \n",
       "21                -1                   0  \n",
       "22                -1                   0  \n",
       "23                -1                   0  \n",
       "24                -1                   0  \n",
       "25                -1                   0  \n",
       "26                -1                   0  \n",
       "27                -1                   0  \n",
       "28                -1                   0  \n",
       "29                -1                   0  \n",
       "...              ...                 ...  \n",
       "31989             -1                   0  \n",
       "31990             -1                   0  \n",
       "31991             -1                   0  \n",
       "31992             -1                   0  \n",
       "31993             -1                   0  \n",
       "31994             -1                   0  \n",
       "31995             -1                   0  \n",
       "31996             -1                   0  \n",
       "31997             -1                   0  \n",
       "31998             -1                   0  \n",
       "31999             -1                   0  \n",
       "32000             -1                   0  \n",
       "32001             -1                   0  \n",
       "32002             -1                   0  \n",
       "32003             -1                   0  \n",
       "32004             -1                   0  \n",
       "32005             -1                   0  \n",
       "32006             -1                   0  \n",
       "32007             -1                   0  \n",
       "32008             -1                   0  \n",
       "32009             -1                   0  \n",
       "32010             -1                   0  \n",
       "32011             -1                   0  \n",
       "32012             -1                   0  \n",
       "32013             -1                   0  \n",
       "32014             -1                   0  \n",
       "32015             -1                   0  \n",
       "32016             -1                   0  \n",
       "32017             -1                   0  \n",
       "32018             -1                   0  \n",
       "\n",
       "[32019 rows x 31 columns]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\ntrain = train.merge(mean_day_wise,on=['air_store_id'],how='left')\\ntest = test.merge(mean_day_wise,on= ['air_store_id'],how='left')\\n\\n\\n\\ntrain = train.merge(n2,on=['air_genre_name'],how='left')\\ntest = test.merge(n2,on= ['air_genre_name'],how='left')\\ntrain = train.merge(n3,on=['air_area_name'],how='left')\\ntest = test.merge(n3,on= ['air_area_name'],how='left')\\n\""
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "train = train.merge(relation,how='left',on='air_store_id')\n",
    "test = test.merge(relation,how='left',on='air_store_id')\n",
    "\n",
    "\n",
    "\n",
    "train = train.merge(hpg_store_info,how='left',on='hpg_store_id')\n",
    "test = test.merge(hpg_store_info,how='left',on='hpg_store_id')\n",
    "\n",
    "train = train.merge(weather,on=['air_store_id','visit_date'],how='left')\n",
    "test = test.merge(weather,on= ['air_store_id','visit_date'],how='left')\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "train = train.merge(mean_day_wise,on=['air_store_id'],how='left')\n",
    "test = test.merge(mean_day_wise,on= ['air_store_id'],how='left')\n",
    "\n",
    "\n",
    "\n",
    "train = train.merge(n2,on=['air_genre_name'],how='left')\n",
    "test = test.merge(n2,on= ['air_genre_name'],how='left')\n",
    "train = train.merge(n3,on=['air_area_name'],how='left')\n",
    "test = test.merge(n3,on= ['air_area_name'],how='left')\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ind1 = train.loc[train.visit_date <= pd.to_datetime('2017-03-30',format= '%Y-%m-%d')].index\n",
    "ind2 = train.loc[train.visit_date > pd.to_datetime('2017-03-30',format= '%Y-%m-%d')].index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nk14 = train.loc[ind1,['visitors','visit_date_dayofw','air_area_name','visit_date_year']].groupby(['visit_date_dayofw','air_area_name','visit_date_year']).agg('mean').reset_index()\\nk14.columns = ['visit_date_dayofw','air_area_name','visit_date_year','mean_visitors7']\\ntrain = train.merge(k14,on=['visit_date_dayofw','air_area_name','visit_date_year'],how='left')\\ntest = test.merge(k14,on= ['visit_date_dayofw','air_area_name','visit_date_year'],how='left')\\n\""
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "k14 = train.loc[ind1,['visitors','visit_date_dayofw','air_area_name','visit_date_year']].groupby(['visit_date_dayofw','air_area_name','visit_date_year']).agg('mean').reset_index()\n",
    "k14.columns = ['visit_date_dayofw','air_area_name','visit_date_year','mean_visitors7']\n",
    "train = train.merge(k14,on=['visit_date_dayofw','air_area_name','visit_date_year'],how='left')\n",
    "test = test.merge(k14,on= ['visit_date_dayofw','air_area_name','visit_date_year'],how='left')\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_tf = list(train.apply(lambda x:'%s %s' % (x['air_area_name'],x['air_genre_name']),axis=1))\n",
    "test_tf = list(test.apply(lambda x:'%s %s' % (x['air_area_name'],x['air_genre_name']),axis=1))\n",
    "train_tf_vec =  tfv.transform(train_tf) \n",
    "test_tf_vec = tfv.transform(test_tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "svd = TruncatedSVD(n_components=50, n_iter=7, random_state=42)\n",
    "svd.fit(train_tf_vec)\n",
    "train_tf_vec = svd.transform(train_tf_vec)\n",
    "test_tf_vec = svd.transform(test_tf_vec)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nauto_data = pd.read_csv('auto_data.csv')\\nauto_data_area = pd.read_csv('auto_data_area_wise.csv')\\ntrain = train.merge(auto_data,how='left',on = 'air_store_id')\\ntest = test.merge(auto_data,how='left',on = 'air_store_id')\\ntrain = train.merge(auto_data_area,how='left',on = 'air_area_name')\\ntest = test.merge(auto_data_area,how='left',on = 'air_area_name')\\n\""
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "auto_data = pd.read_csv('auto_data.csv')\n",
    "auto_data_area = pd.read_csv('auto_data_area_wise.csv')\n",
    "train = train.merge(auto_data,how='left',on = 'air_store_id')\n",
    "test = test.merge(auto_data,how='left',on = 'air_store_id')\n",
    "train = train.merge(auto_data_area,how='left',on = 'air_area_name')\n",
    "test = test.merge(auto_data_area,how='left',on = 'air_area_name')\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_tf_vec = pd.DataFrame(train_tf_vec)\n",
    "test_tf_vec = pd.DataFrame(test_tf_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "air_month_mean = pd.read_csv('air_store_month_wise_mean.csv')\n",
    "air_week_mean = pd.read_csv('air_store_day_of_week_wise_mean.csv')\n",
    "\n",
    "train = train.merge(air_month_mean, how='left',on='air_store_id')\n",
    "test = test.merge(air_month_mean, how='left',on='air_store_id')\n",
    "train = train.merge(air_week_mean, how='left',on='air_store_id')\n",
    "test = test.merge(air_week_mean, how='left',on='air_store_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "air_store_id\n",
      "air_genre_name\n",
      "air_area_name\n",
      "day_of_week\n",
      "hpg_store_id\n"
     ]
    }
   ],
   "source": [
    "train = train.drop(['hpg_area_name','hpg_genre_name','reserve_visitorslist','reserve_datetime','visitors','visit_date'],axis =1)\n",
    "test = test.drop(['hpg_area_name','hpg_genre_name','reserve_visitorslist','reserve_datetime','visitors','visit_date'],axis =1)\n",
    "from sklearn import ensemble, preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "train.replace(np.nan,-1,inplace=True)\n",
    "test.replace(np.nan,-1,inplace=True)\n",
    "text_columns = []\n",
    "for f in train.columns:\n",
    "    if (train[f].dtype == 'object'):  \n",
    "        print f\n",
    "        text_columns.append(f)            \n",
    "        lbl = preprocessing.LabelEncoder()\n",
    "        lbl.fit(list(train[f].values) + list(test[f].values))\n",
    "        train[f] = lbl.transform(list(train[f].values))\n",
    "        test[f] = lbl.transform(list(test[f].values))\n",
    "    \n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\ndf = pd.concat((train[['air_store_id','hpg_store_id']],test[['air_store_id','hpg_store_id']]))\\nprint df.shape,train.shape,test.shape\\none_hot = pd.get_dummies(df['air_store_id'], drop_first=False)\\nk = (one_hot - one_hot.mean()) / one_hot.std()\\nk_train.shape,k_test.shape\\nk_train = k[:train.shape[0]]\\nk_test = k[train.shape[0]:]\\nk = [str(i)+'_col' for i in k_train.columns ]\\nk_train.columns = k\\nk_test.columns = k\\ntrain = pd.concat((train,k_train),axis=1)\\ntest = pd.concat((test,k_test),axis=1)\\ntest.columns\\n[i for i in train.columns if i not in test.columns]\\n\""
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "df = pd.concat((train[['air_store_id','hpg_store_id']],test[['air_store_id','hpg_store_id']]))\n",
    "print df.shape,train.shape,test.shape\n",
    "one_hot = pd.get_dummies(df['air_store_id'], drop_first=False)\n",
    "k = (one_hot - one_hot.mean()) / one_hot.std()\n",
    "k_train.shape,k_test.shape\n",
    "k_train = k[:train.shape[0]]\n",
    "k_test = k[train.shape[0]:]\n",
    "k = [str(i)+'_col' for i in k_train.columns ]\n",
    "k_train.columns = k\n",
    "k_test.columns = k\n",
    "train = pd.concat((train,k_train),axis=1)\n",
    "test = pd.concat((test,k_test),axis=1)\n",
    "test.columns\n",
    "[i for i in train.columns if i not in test.columns]\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.concat((train,train_tf_vec),axis=1)\n",
    "test = pd.concat((test,test_tf_vec),axis=1)\n",
    "train.replace(np.nan,-1,inplace=True)\n",
    "test.replace(np.nan,-1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\ntest['id'] = ids\\ntrain['visitors'] = y\\ntrain.to_csv('final_train.csv',index = False)\\ntest.to_csv('final_test.csv',index = False)\\n\""
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "test['id'] = ids\n",
    "train['visitors'] = y\n",
    "train.to_csv('final_train.csv',index = False)\n",
    "test.to_csv('final_test.csv',index = False)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import  roc_auc_score\n",
    "def runXGB(train_X, train_y, test_X, test_y=None, feature_names=None, seed_val=0,depth =8):\n",
    "        params = {}\n",
    "        params[\"objective\"] = \"reg:linear\"\n",
    "        params['eval_metric'] = 'rmse'\n",
    "        params[\"eta\"] = 0.01 #0.00334\n",
    "        params[\"min_child_weight\"] = 1\n",
    "        params[\"subsample\"] = 0.7\n",
    "        params[\"colsample_bytree\"] = 0.3\n",
    "        params[\"silent\"] = 1\n",
    "        params[\"max_depth\"] = depth\n",
    "        params[\"seed\"] = seed_val\n",
    "        #params[\"max_delta_step\"] = 2\n",
    "        #params[\"gamma\"] = 0.5\n",
    "        num_rounds = 5000 #2500\n",
    "\n",
    "        plst = list(params.items())\n",
    "        xgtrain = xgb.DMatrix(train_X, label=train_y)\n",
    "\n",
    "        if test_y is not None:\n",
    "                xgtest = xgb.DMatrix(test_X, label=test_y)\n",
    "                watchlist = [ (xgtrain,'train'), (xgtest, 'test') ]\n",
    "                model = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stopping_rounds= 300)\n",
    "        else:\n",
    "                xgtest = xgb.DMatrix(test_X)\n",
    "                #xgtest1 = xgb.DMatrix(test_X1)\n",
    "                model = xgb.train(plst, xgtrain, 3200)\n",
    "\n",
    "        if feature_names:\n",
    "                        create_feature_map(feature_names)\n",
    "                        model.dump_model('xgbmodel.txt', 'xgb.fmap', with_stats=True)\n",
    "                        importance = model.get_fscore(fmap='xgb.fmap')\n",
    "                        importance = sorted(importance.items(), key=operator.itemgetter(1), reverse=True)\n",
    "                        imp_df = pd.DataFrame(importance, columns=['feature','fscore'])\n",
    "                        imp_df['fscore'] = imp_df['fscore'] / imp_df['fscore'].sum()\n",
    "                        imp_df.to_csv(\"imp_feat.txt\", index=False)\n",
    "\n",
    "        pred_test_y = model.predict(xgtest)\n",
    "        if test_y is not None:\n",
    "                loss = rmsle(np.expm1(test_y), np.expm1(pred_test_y))\n",
    "        \treturn loss\n",
    "\telse:\n",
    "\t\treturn pred_test_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-rmse:2.41612\ttest-rmse:2.43399\n",
      "Multiple eval metrics have been passed: 'test-rmse' will be used for early stopping.\n",
      "\n",
      "Will train until test-rmse hasn't improved in 300 rounds.\n",
      "[1]\ttrain-rmse:2.3933\ttest-rmse:2.41092\n",
      "[2]\ttrain-rmse:2.3706\ttest-rmse:2.38792\n",
      "[3]\ttrain-rmse:2.34827\ttest-rmse:2.36526\n",
      "[4]\ttrain-rmse:2.32594\ttest-rmse:2.34272\n",
      "[5]\ttrain-rmse:2.30393\ttest-rmse:2.32046\n",
      "[6]\ttrain-rmse:2.28204\ttest-rmse:2.2982\n",
      "[7]\ttrain-rmse:2.26045\ttest-rmse:2.27634\n",
      "[8]\ttrain-rmse:2.23913\ttest-rmse:2.25475\n",
      "[9]\ttrain-rmse:2.21812\ttest-rmse:2.23346\n",
      "[10]\ttrain-rmse:2.19722\ttest-rmse:2.21227\n",
      "[11]\ttrain-rmse:2.17666\ttest-rmse:2.19141\n",
      "[12]\ttrain-rmse:2.1562\ttest-rmse:2.17064\n",
      "[13]\ttrain-rmse:2.13605\ttest-rmse:2.15021\n",
      "[14]\ttrain-rmse:2.11594\ttest-rmse:2.12945\n",
      "[15]\ttrain-rmse:2.09618\ttest-rmse:2.1095\n",
      "[16]\ttrain-rmse:2.07662\ttest-rmse:2.0899\n",
      "[17]\ttrain-rmse:2.05721\ttest-rmse:2.07024\n",
      "[18]\ttrain-rmse:2.03803\ttest-rmse:2.05088\n",
      "[19]\ttrain-rmse:2.01905\ttest-rmse:2.03168\n",
      "[20]\ttrain-rmse:2.00035\ttest-rmse:2.01279\n",
      "[21]\ttrain-rmse:1.98167\ttest-rmse:1.9939\n",
      "[22]\ttrain-rmse:1.96319\ttest-rmse:1.97518\n",
      "[23]\ttrain-rmse:1.94507\ttest-rmse:1.95682\n",
      "[24]\ttrain-rmse:1.92708\ttest-rmse:1.93851\n",
      "[25]\ttrain-rmse:1.9092\ttest-rmse:1.9204\n",
      "[26]\ttrain-rmse:1.89159\ttest-rmse:1.90248\n",
      "[27]\ttrain-rmse:1.87414\ttest-rmse:1.88482\n",
      "[28]\ttrain-rmse:1.8569\ttest-rmse:1.86732\n",
      "[29]\ttrain-rmse:1.83985\ttest-rmse:1.85012\n",
      "[30]\ttrain-rmse:1.82306\ttest-rmse:1.83317\n",
      "[31]\ttrain-rmse:1.80646\ttest-rmse:1.8164\n",
      "[32]\ttrain-rmse:1.79\ttest-rmse:1.79981\n",
      "[33]\ttrain-rmse:1.77365\ttest-rmse:1.78326\n",
      "[34]\ttrain-rmse:1.75739\ttest-rmse:1.76657\n",
      "[35]\ttrain-rmse:1.74146\ttest-rmse:1.75048\n",
      "[36]\ttrain-rmse:1.72565\ttest-rmse:1.73452\n",
      "[37]\ttrain-rmse:1.71008\ttest-rmse:1.71872\n",
      "[38]\ttrain-rmse:1.6945\ttest-rmse:1.70291\n",
      "[39]\ttrain-rmse:1.6791\ttest-rmse:1.68707\n",
      "[40]\ttrain-rmse:1.66398\ttest-rmse:1.67179\n",
      "[41]\ttrain-rmse:1.6489\ttest-rmse:1.65653\n",
      "[42]\ttrain-rmse:1.63398\ttest-rmse:1.6412\n",
      "[43]\ttrain-rmse:1.61932\ttest-rmse:1.62641\n",
      "[44]\ttrain-rmse:1.60473\ttest-rmse:1.61157\n",
      "[45]\ttrain-rmse:1.59048\ttest-rmse:1.59705\n",
      "[46]\ttrain-rmse:1.57639\ttest-rmse:1.58279\n",
      "[47]\ttrain-rmse:1.56227\ttest-rmse:1.56849\n",
      "[48]\ttrain-rmse:1.54852\ttest-rmse:1.55452\n",
      "[49]\ttrain-rmse:1.53494\ttest-rmse:1.54074\n",
      "[50]\ttrain-rmse:1.52139\ttest-rmse:1.52713\n",
      "[51]\ttrain-rmse:1.50786\ttest-rmse:1.51341\n",
      "[52]\ttrain-rmse:1.49462\ttest-rmse:1.50009\n",
      "[53]\ttrain-rmse:1.48158\ttest-rmse:1.48686\n",
      "[54]\ttrain-rmse:1.46852\ttest-rmse:1.47341\n",
      "[55]\ttrain-rmse:1.45574\ttest-rmse:1.46061\n",
      "[56]\ttrain-rmse:1.44312\ttest-rmse:1.448\n",
      "[57]\ttrain-rmse:1.43065\ttest-rmse:1.43535\n",
      "[58]\ttrain-rmse:1.41826\ttest-rmse:1.42291\n",
      "[59]\ttrain-rmse:1.40608\ttest-rmse:1.41071\n",
      "[60]\ttrain-rmse:1.39385\ttest-rmse:1.39835\n",
      "[61]\ttrain-rmse:1.38184\ttest-rmse:1.38628\n",
      "[62]\ttrain-rmse:1.37004\ttest-rmse:1.37443\n",
      "[63]\ttrain-rmse:1.35821\ttest-rmse:1.36248\n",
      "[64]\ttrain-rmse:1.3467\ttest-rmse:1.35084\n",
      "[65]\ttrain-rmse:1.33514\ttest-rmse:1.33886\n",
      "[66]\ttrain-rmse:1.32371\ttest-rmse:1.32708\n",
      "[67]\ttrain-rmse:1.31249\ttest-rmse:1.31581\n",
      "[68]\ttrain-rmse:1.30145\ttest-rmse:1.30455\n",
      "[69]\ttrain-rmse:1.29052\ttest-rmse:1.2935\n",
      "[70]\ttrain-rmse:1.2798\ttest-rmse:1.28273\n",
      "[71]\ttrain-rmse:1.26912\ttest-rmse:1.27197\n",
      "[72]\ttrain-rmse:1.25845\ttest-rmse:1.26113\n",
      "[73]\ttrain-rmse:1.248\ttest-rmse:1.2505\n",
      "[74]\ttrain-rmse:1.23771\ttest-rmse:1.24002\n",
      "[75]\ttrain-rmse:1.22756\ttest-rmse:1.22987\n",
      "[76]\ttrain-rmse:1.21758\ttest-rmse:1.21983\n",
      "[77]\ttrain-rmse:1.20762\ttest-rmse:1.20971\n",
      "[78]\ttrain-rmse:1.19764\ttest-rmse:1.19964\n",
      "[79]\ttrain-rmse:1.18788\ttest-rmse:1.18979\n",
      "[80]\ttrain-rmse:1.17815\ttest-rmse:1.17969\n",
      "[81]\ttrain-rmse:1.16868\ttest-rmse:1.17013\n",
      "[82]\ttrain-rmse:1.15929\ttest-rmse:1.16055\n",
      "[83]\ttrain-rmse:1.14988\ttest-rmse:1.15082\n",
      "[84]\ttrain-rmse:1.1407\ttest-rmse:1.14163\n",
      "[85]\ttrain-rmse:1.13172\ttest-rmse:1.13252\n",
      "[86]\ttrain-rmse:1.12265\ttest-rmse:1.12347\n",
      "[87]\ttrain-rmse:1.1138\ttest-rmse:1.11463\n",
      "[88]\ttrain-rmse:1.10496\ttest-rmse:1.10557\n",
      "[89]\ttrain-rmse:1.09628\ttest-rmse:1.09686\n",
      "[90]\ttrain-rmse:1.08773\ttest-rmse:1.08825\n",
      "[91]\ttrain-rmse:1.07927\ttest-rmse:1.07975\n",
      "[92]\ttrain-rmse:1.07082\ttest-rmse:1.07122\n",
      "[93]\ttrain-rmse:1.06247\ttest-rmse:1.06263\n",
      "[94]\ttrain-rmse:1.05433\ttest-rmse:1.05448\n",
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    {
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    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2002]\ttrain-rmse:0.435943\ttest-rmse:0.48035\n",
      "[2003]\ttrain-rmse:0.43593\ttest-rmse:0.480343\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[3798]\ttrain-rmse:0.410501\ttest-rmse:0.47907\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3799]\ttrain-rmse:0.410485\ttest-rmse:0.479069\n",
      "[3800]\ttrain-rmse:0.410475\ttest-rmse:0.479069\n",
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      "[3837]\ttrain-rmse:0.410029\ttest-rmse:0.479075\n",
      "[3838]\ttrain-rmse:0.41002\ttest-rmse:0.479073\n",
      "[3839]\ttrain-rmse:0.410005\ttest-rmse:0.479078\n",
      "[3840]\ttrain-rmse:0.409989\ttest-rmse:0.479082\n",
      "[3841]\ttrain-rmse:0.409979\ttest-rmse:0.479087\n",
      "[3842]\ttrain-rmse:0.409968\ttest-rmse:0.479083\n",
      "[3843]\ttrain-rmse:0.409952\ttest-rmse:0.479083\n",
      "[3844]\ttrain-rmse:0.409949\ttest-rmse:0.479082\n",
      "[3845]\ttrain-rmse:0.40994\ttest-rmse:0.479082\n",
      "[3846]\ttrain-rmse:0.409927\ttest-rmse:0.479095\n",
      "[3847]\ttrain-rmse:0.409916\ttest-rmse:0.479089\n",
      "[3848]\ttrain-rmse:0.409907\ttest-rmse:0.479097\n",
      "[3849]\ttrain-rmse:0.409896\ttest-rmse:0.479098\n",
      "[3850]\ttrain-rmse:0.409878\ttest-rmse:0.479098\n",
      "[3851]\ttrain-rmse:0.409864\ttest-rmse:0.4791\n",
      "Stopping. Best iteration:\n",
      "[3551]\ttrain-rmse:0.413574\ttest-rmse:0.478949\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train = train.iloc[ind1]\n",
    "X_test = train.iloc[ind2]\n",
    "y_train = y[ind1]\n",
    "y_test  = y[ind2]\n",
    "k = runXGB(X_train, np.log1p(y_train), X_test, np.log1p(y_test))\n",
    "#k = runXGB(X_train, np.log1p(y_train), X_test, np.log1p(y_test))#0.454932\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  -1,   12,    2,    3,   25,    5,    6,    1,   15,   21,    9,\n",
       "         20,   18,   19,   24,    8,    4,   13,   45,   35,   11,   30,\n",
       "         23,   37,   34,    7,   38,   33,   22,   32,   57,   26,   10,\n",
       "         27,   14,   16,   17,   74,   31,   39,   58,   29,   28,   44,\n",
       "         40,   43,   62,   36,   48,   94,   98,   47,   88,   86,   50,\n",
       "        107,  109,   75,   69,   41,   42,   60,   51,   49,   59,   79,\n",
       "         65,   71,   53,   64,   80,   46,   66,   56,   67,   55,  664,\n",
       "        641, 1633,  557,   61,   52,   54,   70,   78,   63,   73,   82,\n",
       "         91,  148,  153,  155,  110,   68,   85,   87,   95,   81,   90,\n",
       "        122,   72,   83,   77,   93,  466,  100,  104,  142,   92,   76,\n",
       "         89,   84,  116,   97,   99,   96,  108])"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Stopping. Best iteration:\n",
    "[3213]\ttrain-rmse:0.415767\ttest-rmse:0.468939\n",
    "Stopping. Best iteration:\n",
    "[3281]\ttrain-rmse:0.410538\ttest-rmse:0.451937\n",
    "[2872]\ttrain-rmse:0.423973\ttest-rmse:0.451862\n",
    "[3020]\ttrain-rmse:0.418175\ttest-rmse:0.452131\n",
    "Stopping. Best iteration:\n",
    "[2866]\ttrain-rmse:0.418617\ttest-rmse:0.452214\n",
    "[3211]\ttrain-rmse:0.434274\ttest-rmse:0.457787\n",
    "[3302]\ttrain-rmse:0.432564\ttest-rmse:0.457957\n",
    "[241]train-rmse:0.441116\ttest-rmse:0.459099\n",
    "[240]\ttrain-rmse:0.441266\ttest-rmse:0.453867\n",
    "[300]\ttrain-rmse:0.444027\ttest-rmse:0.460116\n",
    "[290]\ttrain-rmse:0.46005\ttest-rmse:0.469023\n",
    "train-rmse:0.443398 test-rmse:0.471787\n",
    "[730,6]\ttrain-rmse:0.451892\ttest-rmse:0.471121\n",
    "\"\"\"\n",
    "\n",
    "air_visit_data.total_reserve.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "y1 = np.log1p(y+1)\n",
    "pred1 = runXGB(train,y1, test,depth = 8)\n",
    "\n",
    "p= np.expm1(pred1)-1\n",
    "p[p<0] = 0\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.DataFrame({'id':ids,'visitors':p}).to_csv('last.csv',index =False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['air_store_id', 'air_genre_name', 'air_area_name', 'latitude_x',\n",
       "       'longitude_x', 'holiday_eve', 'non_working', 'genre_in_area',\n",
       "       'total_r_in_area', 'reserve_visitors', 'reserve_-12_h',\n",
       "       'reserve_12_37_h', 'reserve_37_59_h', 'reserve_59_85_h',\n",
       "       'reserve_85+_h', 'visitors_mean', 'visitors_median', 'visitors_max',\n",
       "       'visitors_min', 'visit_date_month', 'visit_date_dayofw',\n",
       "       'visit_date_year', 'visit_date_dayofm', 'weekofyear',\n",
       "       'total_reserve', 'numb_total_reserve', 'mean_visitors',\n",
       "       'median_visitors', 'mean_visitors2', 'mean_visitors4',\n",
       "       'median_visitors2', 'median_visitors4', 'mean_visitors_f',\n",
       "       'mean_visitors3_f', 'mean_visitors4_f', 'no_open_restro',\n",
       "       'day_of_week', 'holiday_flg', 'weight', 'hpg_store_id', 'both',\n",
       "       'latitude_y', 'longitude_y', 'avg_temperature', 'high_temperature',\n",
       "       'low_temperature', 'precipitation', 'm0', 'm1', 'm2', 'm3', 'm6',\n",
       "       'm7', 'm8', 'm9', 'm10', 'm11', 'w1', 'w2', 'w3', 'w4', 'w5', 'w6',\n",
       "       'w7', 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], dtype=object)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.76433253,  21.7926712 ,  25.1543045 , ...,   3.44913435,\n",
       "         3.64120197,   3.79860067], dtype=float32)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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